EP3562380A1 - Système et procédé de réglage d'algorithme appliquant des capteurs de mouvement dans un système cgm - Google Patents

Système et procédé de réglage d'algorithme appliquant des capteurs de mouvement dans un système cgm

Info

Publication number
EP3562380A1
EP3562380A1 EP16924946.3A EP16924946A EP3562380A1 EP 3562380 A1 EP3562380 A1 EP 3562380A1 EP 16924946 A EP16924946 A EP 16924946A EP 3562380 A1 EP3562380 A1 EP 3562380A1
Authority
EP
European Patent Office
Prior art keywords
patient
processer
algorithms
motion sensor
cgm
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP16924946.3A
Other languages
German (de)
English (en)
Other versions
EP3562380A4 (fr
Inventor
Cuijun YANG
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Medtrum Technologies Inc
Original Assignee
Medtrum Technologies Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Medtrum Technologies Inc filed Critical Medtrum Technologies Inc
Publication of EP3562380A1 publication Critical patent/EP3562380A1/fr
Publication of EP3562380A4 publication Critical patent/EP3562380A4/fr
Pending legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14503—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1495—Calibrating or testing of in-vivo probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/7405—Details of notification to user or communication with user or patient ; user input means using sound
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • This invention generally relates to medical appliance, and more specifically to a system and method for algorithm adjustment applying motion sensors in a CGM system.
  • the pancreas produces and releases insulin into the blood stream in response to elevated blood glucose levels.
  • ⁇ -cells which reside in the pancreas, produce and secrete the insulin into the blood stream, as it is needed. If ⁇ -cells become incapacitated or die, a condition known as Type I diabetes mellitus, or in some cases if ⁇ -cells produce insufficient quantities of insulin, Type II diabetes, then insulin must be provided to the body of the patient from another source.
  • infusion pump therapy has been increasing, especially for delivering insulin for diabetics.
  • external infusion pumps are worn on a belt, in a pocket, or patched to the body of the patient directly, and deliver insulin into the body by an infusion tube with a percutaneous needle or a cannula placed in the subcutaneous tissue.
  • the medicine that is delivered by the infusion pump device can depend on the condition of the patient and the desired treatment plan.
  • current insulin pumps and other diabetes treatment instruments are limited in switching between different treatment plans based on different conditions of the patient.
  • Desired treatment plans depend on accurate determination of different conditions of the patient, especially for the continuously glucose monitoring in tissue fluid whose concentration is easily influenced by the actions of the patient. If a patient is in sleep, due to less activity taking place in the muscle and organs than the normal state, whether she or he being in a state of low blood glucose needs to be recalculated by an adjusted algorithm. Furthermore, some low-priority alerts should be muted to prevent disturbing the patient from sleep. Similarly, if a patient is doing physical exercise, her or his blood glucose value may fluctuate sharply, but her or his blood glucose level should not be determined as abnormal, and this false “abnormal fluctuations” of the blood glucoses level should be excluded. In pursuing desirable treatment plans, the combination of sensing the activity level of the patient and adjusting the blood glucose related algorithms to provide more accurate data became crucial.
  • one purpose of the present invention is to provide a method for adjusting blood glucose related algorithms in a continuous glucose monitoring (CGM) system, comprising,
  • the motion sensor comprises one or more from an accelerometer, a gyroscope and an attitude sensor.
  • the method further comprises adjusting the algorithms according to different exercise intensities when the patient is in a state of physical exercise.
  • the blood glucose related algorithms comprise but not limited to a filtering algorithm configured to calculate the blood glucose value, a predictive low glucose algorithm and an alert threshold algorithm.
  • the method further comprises automatically switch the CGM system into an audio-off mode for low-priority alerts that do not require immediate action according to the adjusted algorithm.
  • the method further comprises excluding abnormal fluctuations of the blood glucose sensor data by adjusting related algorithms via the processer when the patient is determined in a state of physical exercise via the processer.
  • the other purpose of the present invention is to provide a system using the method for adjusting blood glucose related algorithms identified above, comprising a CGM system with a processer and at least one motion sensor set in the CGM system.
  • the motion sensor is configured to sense the activity levels of a patient and provide corresponding signals; and the processer is configured to determine the physiological states and exercise intensities of the patient and adjust blood glucose related algorithms depending partly on the signals from the motion sensor.
  • the glucose data processed by the processer using the adjusted algorithm might be sent to a handset or a smart device to display or be further processed to control a patch pump.
  • the present invention has advantages in the following ways: Firstly, applying the motion sensor in the CGM system enables a comprehensive grasp of the patient's activity levels for a more rational treatment by distinguishing sleep and physical exercise states from the normal state, adjusting blood glucose related algorithms according to different activity levels and exercise intensities of the patient provides more reliable data that leads directly to appropriate treatments. Secondly, because the continuously glucose monitoring system detects the glucose level in tissue fluid which is easily influenced by the attitude and activity level of a subject, excluding abnormal fluctuation of the sensor glucose level better reflects the real situation of the patient. Thirdly, muting some low-priority alerts when the patient is determined in the state of sleep or exercise reduces unnecessary disturbance to the patient, making the system more pleasant to use.
  • the application of motion sensor in the CGM system enables algorithm adjustments based on different physical states and exercise intensities of the patient to provide more accurate and reliable blood glucose related data that is the basis of desirable treatment plans, and a CGM system using this method satisfies the requirements of the patient on safety and intelligence of a glucose monitoring device in a more sophisticated way.
  • FIG. 1 is a schematic diagram of a patient wearing a CGM system in the present invention
  • FIG. 2 is a schematic diagram of the CGM system in the present invention
  • FIG. 3 is a schematic diagram of the representative method in an embodiment of the present invention.
  • FIG. 4 is a flow chart of the representative method in an embodiment of the present invention.
  • FIG. 5 is a flow chart of the representative method in an embodiment of the present invention.
  • FIG. 1 illustrates a patient wearing a CGM system 1 configured to monitor the blood glucose changes of the patient in real time.
  • FIG. 2 illustrates the structure of the CGM system 1, comprising a motion sensor 101 and a processer 102.
  • a motion sensor 101 is set in the CGM system 1, configured to sense activity levels of the patient and send corresponding signals to the processer 102.
  • the motion sensor 101 is a three-axis accelerometer 101, sensing the activity levels and state changes of the patient in three axes, and the processer 102 receives signals from the three-axis accelerometer 101 and adjusts corresponding algorithms depending partly on the signals.
  • ACC power is the acceleration amplitude of all three axes
  • ACC X is the acceleration data of the X axis
  • ACC Y is the acceleration data of the Y axis
  • ACC Z is the acceleration data of the Z axis.
  • the attitudes of the patient can be sensed by the three-axis accelerometer 101.
  • the attitude changes of the patient can be tracked by the three-axis accelerometer 101 in real time.
  • the state can be determined by the equation:
  • ACC var (ACC X -ACC X
  • ACC var is the acceleration variation of all three axes
  • ACC X is the acceleration data of the X axis
  • ACC Y is the acceleration data of the Y axis
  • ACC Z is the acceleration data of the Z axis
  • PRE is the acceleration data of the X axis at a previous time
  • PRE is the acceleration data of the Y axis at a previous time
  • PRE is the acceleration data of the Z axis at a previous time.
  • a motion sensor 101 is set in the CGM system 1 to sense activity levels of the patient and send corresponding signals.
  • a processer 102 set in the CGM system 1 receives signals from the motion sensor 101 and adjusts related algorithms depending partly on the signals, and the data processed using the adjusted algorithms is sent to a handset 31 or a smart phone 32 to display.
  • FIG. 4 is a flow chart of an exemplary method illustrating the co-operation of the accelerometer and the processer set in the CGM system.
  • an accelerometer or an attitude sensor in this embodiment, an accelerometer set in the CGM system.
  • the accelerometer senses an activity level of the patient.
  • the accelerometer provides signals indicative of the activity level to a processer in the CGM system.
  • the processer in the CGM system determines whether the patient is in a sleep or physical exercise state according to the signals from the accelerometer.
  • the processer adjusts a series of related algorithms depending partly on the signals from the accelerometer, including but not limited to a filtering algorithm to calculate the blood glucose value as illustrated at block 131, a predictive low glucose algorithm as illustrated at block 132, and an alert threshold algorithm as illustrated at block 133.
  • a filtering algorithm to calculate the blood glucose value as illustrated at block 131
  • a predictive low glucose algorithm as illustrated at block 132
  • an alert threshold algorithm as illustrated at block 133.
  • the processer will automatically switch the CGM system into an audio-off mode as illustrated at block 135, avoiding disturbing the patient in her or his normal sleep or normal exercise.
  • FIG. 5 is a flow chart of an exemplary method illustrating the co-operation of the motion sensor and the processer when the patient is in the state of physical exercise.
  • concentration of her or his tissue fluid may go through instant dramatic changes because of squeezing and stretching actions, so her or his glucose level sensed by a glucose sensor may fluctuate sharply but should not be determined as abnormal.
  • an accelerometer senses a sharp fluctuation in activity level of the patient.
  • the accelerometer provides signals indicative of the activity level to the processer in the CGM system.
  • the processer in the CGM system determines the patient is in a physical exercise state according to the signals from the accelerometer.
  • the processer adjusts a series of algorithms depending partly on the signals from the accelerometer.
  • the processer forbids a calibration of the glucose sensor for the reason that the calibration result would be unreliable during a fast-changing period of the glucose level.
  • the processer excludes the abnormal fluctuation using the adjusted algorithm.
  • the processer allows the calibration of the glucose sensor when the abnormal fluctuation is excluded.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
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  • Optics & Photonics (AREA)
  • Signal Processing (AREA)
  • Emergency Medicine (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • General Business, Economics & Management (AREA)
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  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

L'invention concerne un procédé d'ajustement d'algorithme appliquant un capteur de mouvement dans un système de surveillance de glucose en continu (1) et un système utilisant le procédé, comprenant le fait de détecter un niveau d'activité d'un patient par un capteur de mouvement (101) et de fournir des signaux à un processeur (102), puis d'ajuster une série d'algorithmes apparentés en fonction en partie des signaux par le processeur (102) afin de fournir des données de glycémie plus précises et fiables qui sont la base de plans de traitement souhaitables ; et de faire fonctionner automatiquement le système CGM (1) comprenant la commutation du système vers un mode de désactivation audio ou la mise en pause d'un étalonnage du capteur de glucose par le processeur (102).
EP16924946.3A 2016-12-30 2016-12-30 Système et procédé de réglage d'algorithme appliquant des capteurs de mouvement dans un système cgm Pending EP3562380A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/113676 WO2018120096A1 (fr) 2016-12-30 2016-12-30 Système et procédé de réglage d'algorithme appliquant des capteurs de mouvement dans un système cgm

Publications (2)

Publication Number Publication Date
EP3562380A1 true EP3562380A1 (fr) 2019-11-06
EP3562380A4 EP3562380A4 (fr) 2020-08-12

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Application Number Title Priority Date Filing Date
EP16924946.3A Pending EP3562380A4 (fr) 2016-12-30 2016-12-30 Système et procédé de réglage d'algorithme appliquant des capteurs de mouvement dans un système cgm

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Country Link
US (1) US11412994B2 (fr)
EP (1) EP3562380A4 (fr)
WO (1) WO2018120096A1 (fr)

Families Citing this family (5)

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Publication number Priority date Publication date Assignee Title
WO2011026053A1 (fr) 2009-08-31 2011-03-03 Abbott Diabetes Care Inc. Affichages pour dispositif médical
US9872641B2 (en) 2012-11-29 2018-01-23 Abbott Diabetes Care Inc. Methods, devices, and systems related to analyte monitoring
WO2022040947A1 (fr) * 2020-08-26 2022-03-03 Medtrum Technologies Inc. Système de commande de perfusion d'insuline de pancréas artificiel en boucle fermée
CN114099846A (zh) * 2020-08-26 2022-03-01 上海移宇科技股份有限公司 闭环人工胰腺胰岛素输注控制系统
FR3113824B1 (fr) * 2020-09-09 2023-11-10 Pkvitality Procédé et dispositif de surveillance de concentration en analyte corporel

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WO2004034880A2 (fr) 2002-10-15 2004-04-29 Medtronic Inc. Delai chronometre pour un nouvel apport de therapie de traitement d'un systeme de dispositif medical
US20060241708A1 (en) 2005-04-22 2006-10-26 Willem Boute Multiple sensors for sleep apnea with probability indication for sleep diagnosis and means for automatic activation of alert or therapy
US20080071157A1 (en) * 2006-06-07 2008-03-20 Abbott Diabetes Care, Inc. Analyte monitoring system and method
CN101125086A (zh) * 2006-08-18 2008-02-20 刘胜 闭环自动控制胰岛素注射系统
US8214156B2 (en) 2008-10-31 2012-07-03 Medtronic, Inc. System and method for improving data management between implantable medical devices and external devices
US20100121214A1 (en) 2008-11-11 2010-05-13 Medtronic, Inc. Seizure disorder evaluation based on intracranial pressure and patient motion
JP2012527981A (ja) * 2009-05-29 2012-11-12 ユニバーシティ オブ バージニア パテント ファウンデーション 糖尿病の開ループおよび閉ループ制御のためのシステムコーディネータおよびモジュールアーキテクチャ
JP5997453B2 (ja) * 2011-04-25 2016-09-28 アークレイ株式会社 情報処理装置およびユーザ端末
US20140012117A1 (en) * 2012-07-09 2014-01-09 Dexcom, Inc. Systems and methods for leveraging smartphone features in continuous glucose monitoring
US11534548B2 (en) * 2016-12-30 2022-12-27 Medtrum Technologies Inc. System and method for a closed loop control in an artificial pancreas

Also Published As

Publication number Publication date
US11412994B2 (en) 2022-08-16
WO2018120096A1 (fr) 2018-07-05
US20190328340A1 (en) 2019-10-31
EP3562380A4 (fr) 2020-08-12

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